code: def _prepare_data():
      df = pd.read_csv(
          "tests/test_data/binary/data2.csv.gz", compression="gzip"
      )
      X, y = df[["feature1", "feature2"]], df["target"]
      cv = BlockedTimeSeriesSplit(n_splits=5, test_size=0.25)
      df_etalon_stat = pd.read_csv(
          "tests/test_data/binary/blocked_ts_logic.csv"
      )
      model = LogisticRegression(random_state=0)
  
      return X, y, cv, df_etalon_stat, model
  
file_path: upgini/tests/test_blocked_time_series.py
imports: import re
  import pandas
  import pytest
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import cross_val_score
  from upgini.resource_bundle import bundle
  from upgini.utils.blocked_time_series import BlockedTimeSeriesSplit
label_desc: Use image node_id 1 for calling a global function with example usage: _prepare_data() and returns: X,  y,  cv,  df_etalon_stat,  model